{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as tud\n",
    "from torch.nn.parameter import Parameter\n",
    "\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import sklearn\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了保证实验结果可以复现，我们经常会把各种random seed固定在某一个值\n",
    "random.seed(53113)\n",
    "np.random.seed(53113)\n",
    "torch.manual_seed(53113)\n",
    "    \n",
    "# 设定一些超参数\n",
    "    \n",
    "K = 100 # number of negative samples\n",
    "C = 3 # nearby words threshold\n",
    "NUM_EPOCHS = 2 # The number of epochs of training\n",
    "MAX_VOCAB_SIZE = 30000 # the vocabulary size\n",
    "BATCH_SIZE = 128 # the batch size\n",
    "LEARNING_RATE = 0.2 # the initial learning rate\n",
    "EMBEDDING_SIZE = 100\n",
    "       \n",
    "    \n",
    "LOG_FILE = \"word-embedding.log\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 为了保证实验结果可以复现，我们经常会把各种random seed固定在某一个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"./data/text8/text8.train.txt\", \"r\") as fin:\n",
    "    text = fin.read()\n",
    "    \n",
    "text = [w for w in text.split()]\n",
    "vocab = dict(Counter(text).most_common(MAX_VOCAB_SIZE-1))  #选取MAX_VOCAB_SIZE个单词，统计\n",
    "vocab[\"<unk>\"] = len(text) - np.sum(list(vocab.values()))  #添加一个UNK单词表示所有不常见的单词\n",
    "#print(vocab)\n",
    "idx_to_word = [word for word in vocab.keys()]  #记录单词到index的mapping\n",
    "word_to_idx = {word:i for i, word in enumerate(idx_to_word)} #index到单词的mapping\n",
    "\n",
    "word_counts = np.array([count for count in vocab.values()], dtype=np.float32)  #单词的count\n",
    "word_freqs = word_counts / np.sum(word_counts)   #np.sum(word_counts) 单词的总数\n",
    "word_freqs = word_freqs ** (3./4.)     #单词的(normalized) frequency\n",
    "word_freqs = word_freqs / np.sum(word_freqs) # 用来做 negative sampling\n",
    "VOCAB_SIZE = len(idx_to_word)\n",
    "VOCAB_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WordEmbeddingDataset(tud.Dataset):\n",
    "    def __init__(self, text, word_to_idx, idx_to_word, word_freqs, word_counts):\n",
    "        ''' text: a list of words, all text from the training dataset\n",
    "            word_to_idx: the dictionary from word to idx\n",
    "            idx_to_word: idx to word mapping\n",
    "            word_freq: the frequency of each word\n",
    "            word_counts: the word counts\n",
    "        '''\n",
    "        super(WordEmbeddingDataset, self).__init__()\n",
    "        self.text_encoded = [word_to_idx.get(t, VOCAB_SIZE-1) for t in text]\n",
    "        self.text_encoded = torch.Tensor(self.text_encoded).long()\n",
    "        self.word_to_idx = word_to_idx\n",
    "        self.idx_to_word = idx_to_word\n",
    "        self.word_freqs = torch.Tensor(word_freqs)\n",
    "        self.word_counts = torch.Tensor(word_counts)\n",
    "    \n",
    "    def __len__(self):  #__len__ function需要返回整个数据集中有多少个item\n",
    "        ''' 返回整个数据集（所有单词）的长度\n",
    "        '''\n",
    "        return len(self.text_encoded)\n",
    "        \n",
    "    def __getitem__(self, idx): #__getitem__ 根据给定的index返回一个item\n",
    "        ''' 这个function返回以下数据用于训练\n",
    "            - 中心词\n",
    "            - 这个单词附近的(positive)单词\n",
    "            - 随机采样的K个单词作为negative sample\n",
    "        '''\n",
    "        center_word = self.text_encoded[idx]\n",
    "        pos_indices = list(range(idx-C, idx)) + list(range(idx+1, idx+C+1))\n",
    "        pos_indices = [i%len(self.text_encoded) for i in pos_indices]\n",
    "        pos_words = self.text_encoded[pos_indices] \n",
    "        neg_words = torch.multinomial(self.word_freqs, K * pos_words.shape[0], True)\n",
    "        \n",
    "        return center_word, pos_words, neg_words "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建dataset和dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = WordEmbeddingDataset(text, word_to_idx, idx_to_word, word_freqs, word_counts)\n",
    "dataloader = tud.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义PyTorch模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EmbeddingModel(nn.Module):\n",
    "    def __init__(self, vocab_size, embed_size):\n",
    "        ''' 初始化输出和输出embedding\n",
    "        '''\n",
    "        super(EmbeddingModel, self).__init__()\n",
    "        self.vocab_size = vocab_size\n",
    "        self.embed_size = embed_size\n",
    "        \n",
    "        initrange = 0.5 / self.embed_size\n",
    "        self.out_embed = nn.Embedding(self.vocab_size, self.embed_size, sparse=False)\n",
    "        self.out_embed.weight.data.uniform_(-initrange, initrange)\n",
    "        \n",
    "        \n",
    "        self.in_embed = nn.Embedding(self.vocab_size, self.embed_size, sparse=False)\n",
    "        self.in_embed.weight.data.uniform_(-initrange, initrange)\n",
    "        \n",
    "        \n",
    "    def forward(self, input_labels, pos_labels, neg_labels):\n",
    "        '''\n",
    "        input_labels: 中心词, [batch_size]\n",
    "        pos_labels: 中心词周围 context window 出现过的单词 [batch_size * (window_size * 2)]\n",
    "        neg_labelss: 中心词周围没有出现过的单词，从 negative sampling 得到 [batch_size, (window_size * 2 * K)]\n",
    "        \n",
    "        return: loss, [batch_size]\n",
    "        '''\n",
    "        \n",
    "        batch_size = input_labels.size(0)\n",
    "        \n",
    "        input_embedding = self.in_embed(input_labels) # B * embed_size\n",
    "        pos_embedding = self.out_embed(pos_labels) # B * (2*C) * embed_size\n",
    "        neg_embedding = self.out_embed(neg_labels) # B * (2*C * K) * embed_size\n",
    "      \n",
    "        log_pos = torch.bmm(pos_embedding, input_embedding.unsqueeze(2)).squeeze() # B * (2*C)\n",
    "        log_neg = torch.bmm(neg_embedding, -input_embedding.unsqueeze(2)).squeeze() # B * (2*C*K)\n",
    "\n",
    "        log_pos = F.logsigmoid(log_pos).sum(1)\n",
    "        log_neg = F.logsigmoid(log_neg).sum(1) # batch_size\n",
    "       \n",
    "        loss = log_pos + log_neg\n",
    "        \n",
    "        return -loss\n",
    "    \n",
    "    def input_embeddings(self):\n",
    "        return self.in_embed.weight.data.numpy()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = EmbeddingModel(VOCAB_SIZE, EMBEDDING_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "下面是评估模型的代码，以及训练模型的代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate(filename, embedding_weights): \n",
    "    if filename.endswith(\".csv\"):\n",
    "        data = pd.read_csv(filename, sep=\",\")\n",
    "    else:\n",
    "        data = pd.read_csv(filename, sep=\"\\t\")\n",
    "    human_similarity = []\n",
    "    model_similarity = []\n",
    "    for i in data.iloc[:, 0:2].index:\n",
    "        word1, word2 = data.iloc[i, 0], data.iloc[i, 1]\n",
    "        if word1 not in word_to_idx or word2 not in word_to_idx:\n",
    "            continue\n",
    "        else:\n",
    "            word1_idx, word2_idx = word_to_idx[word1], word_to_idx[word2]\n",
    "            word1_embed, word2_embed = embedding_weights[[word1_idx]], embedding_weights[[word2_idx]]\n",
    "            model_similarity.append(float(sklearn.metrics.pairwise.cosine_similarity(word1_embed, word2_embed)))\n",
    "            human_similarity.append(float(data.iloc[i, 2]))\n",
    "\n",
    "    return scipy.stats.spearmanr(human_similarity, model_similarity)# , model_similarity\n",
    "\n",
    "def find_nearest(word):\n",
    "    index = word_to_idx[word]\n",
    "    embedding = embedding_weights[index]\n",
    "    cos_dis = np.array([scipy.spatial.distance.cosine(e, embedding) for e in embedding_weights])\n",
    "    return [idx_to_word[i] for i in cos_dis.argsort()[:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, iter: 0, loss: 420.04736328125\n",
      "epoch: 0, iteration: 0, simlex-999: SpearmanrResult(correlation=0.002806243285464091, pvalue=0.9309107582703205), men: SpearmanrResult(correlation=-0.03578915454199749, pvalue=0.06854012381329619), sim353: SpearmanrResult(correlation=0.02468906830123471, pvalue=0.6609497549092586), nearest to monster: ['monster', 'communism', 'bosses', 'microprocessors', 'infectious', 'debussy', 'unesco', 'tantamount', 'offices', 'tischendorf']\n",
      "\n",
      "epoch: 0, iter: 100, loss: 278.9967041015625\n",
      "epoch: 0, iter: 200, loss: 248.71990966796875\n",
      "epoch: 0, iter: 300, loss: 202.95816040039062\n",
      "epoch: 0, iter: 400, loss: 157.04776000976562\n",
      "epoch: 0, iter: 500, loss: 137.83531188964844\n",
      "epoch: 0, iter: 600, loss: 121.03585815429688\n",
      "epoch: 0, iter: 700, loss: 105.300537109375\n",
      "epoch: 0, iter: 800, loss: 114.10055541992188\n",
      "epoch: 0, iter: 900, loss: 104.72723388671875\n",
      "epoch: 0, iter: 1000, loss: 99.03569030761719\n",
      "epoch: 0, iter: 1100, loss: 95.2179946899414\n",
      "epoch: 0, iter: 1200, loss: 84.12557983398438\n",
      "epoch: 0, iter: 1300, loss: 88.07209777832031\n",
      "epoch: 0, iter: 1400, loss: 70.44454193115234\n",
      "epoch: 0, iter: 1500, loss: 79.83641052246094\n",
      "epoch: 0, iter: 1600, loss: 81.7451171875\n",
      "epoch: 0, iter: 1700, loss: 75.91305541992188\n",
      "epoch: 0, iter: 1800, loss: 65.86140441894531\n",
      "epoch: 0, iter: 1900, loss: 69.81714630126953\n",
      "epoch: 0, iter: 2000, loss: 71.05166625976562\n",
      "epoch: 0, iteration: 2000, simlex-999: SpearmanrResult(correlation=-0.011490367338787073, pvalue=0.7225847577400916), men: SpearmanrResult(correlation=0.05671509287050605, pvalue=0.0038790264864563434), sim353: SpearmanrResult(correlation=-0.07381419228558825, pvalue=0.18921537418718104), nearest to monster: ['monster', 'harm', 'steel', 'dean', 'kansas', 'surgery', 'regardless', 'capitalism', 'offers', 'hockey']\n",
      "\n",
      "epoch: 0, iter: 2100, loss: 59.19840621948242\n",
      "epoch: 0, iter: 2200, loss: 60.21418762207031\n",
      "epoch: 0, iter: 2300, loss: 63.848148345947266\n",
      "epoch: 0, iter: 2400, loss: 65.58479309082031\n",
      "epoch: 0, iter: 2500, loss: 66.90382385253906\n",
      "epoch: 0, iter: 2600, loss: 54.61847686767578\n",
      "epoch: 0, iter: 2700, loss: 56.45966339111328\n",
      "epoch: 0, iter: 2800, loss: 58.255210876464844\n",
      "epoch: 0, iter: 2900, loss: 59.65287399291992\n",
      "epoch: 0, iter: 3000, loss: 48.22801971435547\n",
      "epoch: 0, iter: 3100, loss: 42.94969177246094\n",
      "epoch: 0, iter: 3200, loss: 49.372528076171875\n",
      "epoch: 0, iter: 3300, loss: 46.12495422363281\n",
      "epoch: 0, iter: 3400, loss: 58.97121047973633\n",
      "epoch: 0, iter: 3500, loss: 48.31055450439453\n",
      "epoch: 0, iter: 3600, loss: 47.07227325439453\n",
      "epoch: 0, iter: 3700, loss: 46.4068603515625\n",
      "epoch: 0, iter: 3800, loss: 49.55707931518555\n",
      "epoch: 0, iter: 3900, loss: 44.38733673095703\n",
      "epoch: 0, iter: 4000, loss: 48.730342864990234\n",
      "epoch: 0, iteration: 4000, simlex-999: SpearmanrResult(correlation=0.0190424235850696, pvalue=0.5562848091306694), men: SpearmanrResult(correlation=0.05404895260610133, pvalue=0.00592548586032086), sim353: SpearmanrResult(correlation=-0.039572591538143916, pvalue=0.4819454801463242), nearest to monster: ['monster', 'electrical', 'northeast', 'surgery', 'entity', 'certainly', 'tea', 'establishing', 'archbishop', 'aging']\n",
      "\n",
      "epoch: 0, iter: 4100, loss: 57.70344161987305\n",
      "epoch: 0, iter: 4200, loss: 47.464820861816406\n",
      "epoch: 0, iter: 4300, loss: 47.08036804199219\n",
      "epoch: 0, iter: 4400, loss: 46.652706146240234\n",
      "epoch: 0, iter: 4500, loss: 40.824310302734375\n",
      "epoch: 0, iter: 4600, loss: 40.62211227416992\n",
      "epoch: 0, iter: 4700, loss: 50.84752655029297\n",
      "epoch: 0, iter: 4800, loss: 41.230072021484375\n",
      "epoch: 0, iter: 4900, loss: 53.74473571777344\n",
      "epoch: 0, iter: 5000, loss: 42.35053253173828\n",
      "epoch: 0, iter: 5100, loss: 38.363189697265625\n",
      "epoch: 0, iter: 5200, loss: 42.772552490234375\n",
      "epoch: 0, iter: 5300, loss: 44.914913177490234\n",
      "epoch: 0, iter: 5400, loss: 38.4688720703125\n",
      "epoch: 0, iter: 5500, loss: 41.0843391418457\n",
      "epoch: 0, iter: 5600, loss: 35.04629898071289\n",
      "epoch: 0, iter: 5700, loss: 35.49506759643555\n",
      "epoch: 0, iter: 5800, loss: 36.009666442871094\n",
      "epoch: 0, iter: 5900, loss: 40.56498718261719\n",
      "epoch: 0, iter: 6000, loss: 45.853214263916016\n",
      "epoch: 0, iteration: 6000, simlex-999: SpearmanrResult(correlation=0.04213372810279324, pvalue=0.19281410892481102), men: SpearmanrResult(correlation=0.06483263975087832, pvalue=0.0009600352172924885), sim353: SpearmanrResult(correlation=-0.015385630136134733, pvalue=0.7846219761829791), nearest to monster: ['monster', 'raw', 'romantic', 'oregon', 'protest', 'brunei', 'cartoon', 'offers', 'certainly', 'ill']\n",
      "\n",
      "epoch: 0, iter: 6100, loss: 39.977508544921875\n",
      "epoch: 0, iter: 6200, loss: 35.47979736328125\n",
      "epoch: 0, iter: 6300, loss: 38.61311340332031\n",
      "epoch: 0, iter: 6400, loss: 38.735679626464844\n",
      "epoch: 0, iter: 6500, loss: 41.1725959777832\n",
      "epoch: 0, iter: 6600, loss: 37.390037536621094\n",
      "epoch: 0, iter: 6700, loss: 39.51911926269531\n",
      "epoch: 0, iter: 6800, loss: 47.12213897705078\n",
      "epoch: 0, iter: 6900, loss: 41.91630172729492\n",
      "epoch: 0, iter: 7000, loss: 38.11504364013672\n",
      "epoch: 0, iter: 7100, loss: 38.12763214111328\n",
      "epoch: 0, iter: 7200, loss: 36.93813705444336\n",
      "epoch: 0, iter: 7300, loss: 40.82877731323242\n",
      "epoch: 0, iter: 7400, loss: 36.211429595947266\n",
      "epoch: 0, iter: 7500, loss: 36.141693115234375\n",
      "epoch: 0, iter: 7600, loss: 38.152610778808594\n",
      "epoch: 0, iter: 7700, loss: 38.90789031982422\n",
      "epoch: 0, iter: 7800, loss: 36.30712127685547\n",
      "epoch: 0, iter: 7900, loss: 34.192440032958984\n",
      "epoch: 0, iter: 8000, loss: 39.182212829589844\n",
      "epoch: 0, iteration: 8000, simlex-999: SpearmanrResult(correlation=0.05506138271487322, pvalue=0.0886781241789579), men: SpearmanrResult(correlation=0.06796632118931804, pvalue=0.0005362832465382729), sim353: SpearmanrResult(correlation=-0.00727317983344893, pvalue=0.897207043425527), nearest to monster: ['monster', 'raw', 'romantic', 'strategic', 'offers', 'invited', 'signature', 'piano', 'protest', 'bills']\n",
      "\n",
      "epoch: 0, iter: 8100, loss: 35.08313751220703\n",
      "epoch: 0, iter: 8200, loss: 33.23561096191406\n",
      "epoch: 0, iter: 8300, loss: 36.047096252441406\n",
      "epoch: 0, iter: 8400, loss: 37.01750946044922\n",
      "epoch: 0, iter: 8500, loss: 33.679561614990234\n",
      "epoch: 0, iter: 8600, loss: 36.492515563964844\n",
      "epoch: 0, iter: 8700, loss: 34.439537048339844\n",
      "epoch: 0, iter: 8800, loss: 38.89817428588867\n",
      "epoch: 0, iter: 8900, loss: 34.17725372314453\n",
      "epoch: 0, iter: 9000, loss: 33.869651794433594\n",
      "epoch: 0, iter: 9100, loss: 33.63176727294922\n",
      "epoch: 0, iter: 9200, loss: 35.203460693359375\n",
      "epoch: 0, iter: 9300, loss: 36.060142517089844\n",
      "epoch: 0, iter: 9400, loss: 35.6544303894043\n",
      "epoch: 0, iter: 9500, loss: 35.01182556152344\n",
      "epoch: 0, iter: 9600, loss: 35.48432540893555\n",
      "epoch: 0, iter: 9700, loss: 34.940696716308594\n",
      "epoch: 0, iter: 9800, loss: 33.99235534667969\n",
      "epoch: 0, iter: 9900, loss: 35.14078903198242\n",
      "epoch: 0, iter: 10000, loss: 34.10219192504883\n",
      "epoch: 0, iteration: 10000, simlex-999: SpearmanrResult(correlation=0.0714732189475033, pvalue=0.02703637716635098), men: SpearmanrResult(correlation=0.07013186360584196, pvalue=0.00035356323424747736), sim353: SpearmanrResult(correlation=-0.0013966072615024432, pvalue=0.9802088977698729), nearest to monster: ['monster', 'adoption', 'logo', 'particle', 'isle', 'remainder', 'profit', 'rank', 'execution', 'outer']\n",
      "\n",
      "epoch: 0, iter: 10100, loss: 33.885284423828125\n",
      "epoch: 0, iter: 10200, loss: 39.90406036376953\n",
      "epoch: 0, iter: 10300, loss: 34.071014404296875\n",
      "epoch: 0, iter: 10400, loss: 35.23554229736328\n",
      "epoch: 0, iter: 10500, loss: 35.033878326416016\n",
      "epoch: 0, iter: 10600, loss: 36.56634521484375\n",
      "epoch: 0, iter: 10700, loss: 34.755027770996094\n",
      "epoch: 0, iter: 10800, loss: 37.447967529296875\n",
      "epoch: 0, iter: 10900, loss: 37.32883834838867\n",
      "epoch: 0, iter: 11000, loss: 34.621700286865234\n",
      "epoch: 0, iter: 11100, loss: 34.79033660888672\n",
      "epoch: 0, iter: 11200, loss: 33.45790100097656\n",
      "epoch: 0, iter: 11300, loss: 34.915672302246094\n",
      "epoch: 0, iter: 11400, loss: 33.67906188964844\n",
      "epoch: 0, iter: 11500, loss: 33.42378616333008\n",
      "epoch: 0, iter: 11600, loss: 33.216270446777344\n",
      "epoch: 0, iter: 11700, loss: 35.964393615722656\n",
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      "\n"
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    {
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     ]
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    {
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      "\n",
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     ]
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    {
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     ]
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    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
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    {
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     "output_type": "stream",
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      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "epoch: 1, iter: 115900, loss: 30.42316436767578\n",
      "epoch: 1, iter: 116000, loss: 30.36935043334961\n",
      "epoch: 1, iteration: 116000, simlex-999: SpearmanrResult(correlation=0.17049890390424202, pvalue=1.1177389455317111e-07), men: SpearmanrResult(correlation=0.1761607002941838, pvalue=1.6655510704355566e-19), sim353: SpearmanrResult(correlation=0.26764518119690817, pvalue=1.2812867355896495e-06), nearest to monster: ['monster', 'giant', 'bull', 'clown', 'robot', 'hammer', 'killer', 'triangle', 'vampire', 'demon']\n",
      "\n",
      "epoch: 1, iter: 116100, loss: 29.956588745117188\n",
      "epoch: 1, iter: 116200, loss: 30.239765167236328\n",
      "epoch: 1, iter: 116300, loss: 30.056724548339844\n",
      "epoch: 1, iter: 116400, loss: 30.337177276611328\n",
      "epoch: 1, iter: 116500, loss: 30.554336547851562\n",
      "epoch: 1, iter: 116600, loss: 30.801679611206055\n",
      "epoch: 1, iter: 116700, loss: 30.705276489257812\n",
      "epoch: 1, iter: 116800, loss: 30.503780364990234\n",
      "epoch: 1, iter: 116900, loss: 29.62342643737793\n",
      "epoch: 1, iter: 117000, loss: 30.29004669189453\n",
      "epoch: 1, iter: 117100, loss: 30.506996154785156\n",
      "epoch: 1, iter: 117200, loss: 30.6331787109375\n",
      "epoch: 1, iter: 117300, loss: 30.65314483642578\n",
      "epoch: 1, iter: 117400, loss: 30.795137405395508\n",
      "epoch: 1, iter: 117500, loss: 30.28030776977539\n",
      "epoch: 1, iter: 117600, loss: 30.351322174072266\n",
      "epoch: 1, iter: 117700, loss: 30.542030334472656\n",
      "epoch: 1, iter: 117800, loss: 30.361120223999023\n",
      "epoch: 1, iter: 117900, loss: 30.456024169921875\n",
      "epoch: 1, iter: 118000, loss: 30.537174224853516\n",
      "epoch: 1, iteration: 118000, simlex-999: SpearmanrResult(correlation=0.17280547641221922, pvalue=7.475906386705914e-08), men: SpearmanrResult(correlation=0.17732985405232565, pvalue=9.526064095701539e-20), sim353: SpearmanrResult(correlation=0.2698694905041053, pvalue=1.037261623872224e-06), nearest to monster: ['monster', 'giant', 'robot', 'clown', 'hammer', 'bull', 'killer', 'vampire', 'triangle', 'demon']\n",
      "\n",
      "epoch: 1, iter: 118100, loss: 29.840484619140625\n",
      "epoch: 1, iter: 118200, loss: 30.325119018554688\n",
      "epoch: 1, iter: 118300, loss: 30.51473045349121\n",
      "epoch: 1, iter: 118400, loss: 30.261699676513672\n",
      "epoch: 1, iter: 118500, loss: 30.180068969726562\n",
      "epoch: 1, iter: 118600, loss: 30.017879486083984\n",
      "epoch: 1, iter: 118700, loss: 30.56424903869629\n",
      "epoch: 1, iter: 118800, loss: 30.457590103149414\n",
      "epoch: 1, iter: 118900, loss: 30.63213539123535\n",
      "epoch: 1, iter: 119000, loss: 30.692546844482422\n",
      "epoch: 1, iter: 119100, loss: 30.539554595947266\n",
      "epoch: 1, iter: 119200, loss: 30.656726837158203\n",
      "epoch: 1, iter: 119300, loss: 30.380685806274414\n",
      "epoch: 1, iter: 119400, loss: 29.897314071655273\n",
      "epoch: 1, iter: 119500, loss: 29.90090560913086\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)\n",
    "for e in range(NUM_EPOCHS):\n",
    "    for i, (input_labels, pos_labels, neg_labels) in enumerate(dataloader):\n",
    "        \n",
    "        \n",
    "        # TODO\n",
    "        input_labels = input_labels.long()\n",
    "        pos_labels = pos_labels.long()\n",
    "        neg_labels = neg_labels.long()\n",
    "        if USE_CUDA:\n",
    "            input_labels = input_labels.cuda()\n",
    "            pos_labels = pos_labels.cuda()\n",
    "            neg_labels = neg_labels.cuda()\n",
    "            \n",
    "        optimizer.zero_grad()\n",
    "        loss = model(input_labels, pos_labels, neg_labels).mean()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if i % 100 == 0:\n",
    "            with open(LOG_FILE, \"a\") as fout:\n",
    "                fout.write(\"epoch: {}, iter: {}, loss: {}\\n\".format(e, i, loss.item()))\n",
    "                print(\"epoch: {}, iter: {}, loss: {}\".format(e, i, loss.item()))\n",
    "            \n",
    "        \n",
    "        if i % 2000 == 0:\n",
    "            embedding_weights = model.input_embeddings()\n",
    "            sim_simlex = evaluate(\"simlex-999.txt\", embedding_weights)\n",
    "            sim_men = evaluate(\"men.txt\", embedding_weights)\n",
    "            sim_353 = evaluate(\"wordsim353.csv\", embedding_weights)\n",
    "            with open(LOG_FILE, \"a\") as fout:\n",
    "                print(\"epoch: {}, iteration: {}, simlex-999: {}, men: {}, sim353: {}, nearest to monster: {}\\n\".format(\n",
    "                    e, i, sim_simlex, sim_men, sim_353, find_nearest(\"monster\")))\n",
    "                fout.write(\"epoch: {}, iteration: {}, simlex-999: {}, men: {}, sim353: {}, nearest to monster: {}\\n\".format(\n",
    "                    e, i, sim_simlex, sim_men, sim_353, find_nearest(\"monster\")))\n",
    "                \n",
    "    embedding_weights = model.input_embeddings()\n",
    "    np.save(\"embedding-{}\".format(EMBEDDING_SIZE), embedding_weights)\n",
    "    torch.save(model.state_dict(), \"embedding-{}.th\".format(EMBEDDING_SIZE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_state_dict(torch.load(\"embedding-{}.th\".format(EMBEDDING_SIZE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在 MEN 和 Simplex-999 数据集上做评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simlex-999 SpearmanrResult(correlation=0.17251697429101504, pvalue=7.863946056740345e-08)\n",
      "men SpearmanrResult(correlation=0.1778096817088841, pvalue=7.565661657312768e-20)\n",
      "wordsim353 SpearmanrResult(correlation=0.27153702278146635, pvalue=8.842165885381714e-07)\n"
     ]
    }
   ],
   "source": [
    "embedding_weights = model.input_embeddings()\n",
    "print(\"simlex-999\", evaluate(\"simlex-999.txt\", embedding_weights))\n",
    "print(\"men\", evaluate(\"men.txt\", embedding_weights))\n",
    "print(\"wordsim353\", evaluate(\"wordsim353.csv\", embedding_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 寻找nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good ['good', 'bad', 'perfect', 'hard', 'questions', 'alone', 'money', 'false', 'truth', 'experience']\n",
      "fresh ['fresh', 'grain', 'waste', 'cooling', 'lighter', 'dense', 'mild', 'sized', 'warm', 'steel']\n",
      "monster ['monster', 'giant', 'robot', 'hammer', 'clown', 'bull', 'demon', 'triangle', 'storyline', 'slogan']\n",
      "green ['green', 'blue', 'yellow', 'white', 'cross', 'orange', 'black', 'red', 'mountain', 'gold']\n",
      "like ['like', 'unlike', 'etc', 'whereas', 'animals', 'soft', 'amongst', 'similarly', 'bear', 'drink']\n",
      "america ['america', 'africa', 'korea', 'india', 'australia', 'turkey', 'pakistan', 'mexico', 'argentina', 'carolina']\n",
      "chicago ['chicago', 'boston', 'illinois', 'texas', 'london', 'indiana', 'massachusetts', 'florida', 'berkeley', 'michigan']\n",
      "work ['work', 'writing', 'job', 'marx', 'solo', 'label', 'recording', 'nietzsche', 'appearance', 'stage']\n",
      "computer ['computer', 'digital', 'electronic', 'audio', 'video', 'graphics', 'hardware', 'software', 'computers', 'program']\n",
      "language ['language', 'languages', 'alphabet', 'arabic', 'grammar', 'pronunciation', 'dialect', 'programming', 'chinese', 'spelling']\n"
     ]
    }
   ],
   "source": [
    "for word in [\"good\", \"fresh\", \"monster\", \"green\", \"like\", \"america\", \"chicago\", \"work\", \"computer\", \"language\"]:\n",
    "    print(word, find_nearest(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单词之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "king\n",
      "henry\n",
      "charles\n",
      "pope\n",
      "queen\n",
      "iii\n",
      "prince\n",
      "elizabeth\n",
      "alexander\n",
      "constantine\n",
      "edward\n",
      "son\n",
      "iv\n",
      "louis\n",
      "emperor\n",
      "mary\n",
      "james\n",
      "joseph\n",
      "frederick\n",
      "francis\n"
     ]
    }
   ],
   "source": [
    "man_idx = word_to_idx[\"man\"] \n",
    "king_idx = word_to_idx[\"king\"] \n",
    "woman_idx = word_to_idx[\"woman\"]\n",
    "embedding = embedding_weights[woman_idx] - embedding_weights[man_idx] + embedding_weights[king_idx]\n",
    "cos_dis = np.array([scipy.spatial.distance.cosine(e, embedding) for e in embedding_weights])\n",
    "for i in cos_dis.argsort()[:20]:\n",
    "    print(idx_to_word[i])"
   ]
  }
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